Person Recognition Based on FaceNet under Simulated Prosthetic Vision

Abstract

Face information is important information for identifying people, but at low resolution, face information is not well recognized. A psychological physics experiment of person recognition in daily life was designed to discover the best strategy for the limited number of stimulating electrodes, and provide useful visual information. The real-time image-processing strategy based on FaceNet were used to optimize the person information by turning complex face information into simple Chinese character information. Noted that all processed target faces were obscured by separate Chinese characters which are the target people’s last names. The psychological results showed that the image-processing strategy based on FaceNet improved recognition accuracy. The proposed strategy, to convert complex face information into simple Chinese character information, could help subjects to use their own first knowledge to identify the person who need to be identified more faster and accurately. CCS Concepts •Human-centered computing ➝ Human computer interaction; •Computing methodologies ➝ Artificial intelligence ➝ Computer vision ➝ Computer vision tasks; •Social and professional topics

Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

“…In recent years, it has become a trend to train models to complete face recognition based on the deep neural network using larger labeled datasets. For example, Google used 200 million face images containing 8 million different individuals in FaceNet [8]. However, the cost of collecting and tagging such datasets is enormous, and training them through deep neural networks requires better hardware support.…”